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intersection of mechanics, materials, and machine learning. Collaboration with international experts from diverse disciplines. Access to cutting-edge computational and experimental facilities. Supervision of MSc
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, you will combine continuum mechanics, machine learning, experimental imaging, and mechanical testing to design and characterize self-sensing soft matter capable of revealing its mechanics from
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cutting-edge methods in chemical, mechanical, and plasma processing of metal ores for a truly circular economy. Correlating experimental, ab initio and multi-scale simulation as well as machine learning
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated
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dynamics and heat transfer research. Programming skills in Python and MATLAB, particularly in machine learning, data analysis, and image processing. Experience working in Linux environments. Ability
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large
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. dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield. His research interests include remote sensing image processing, computer vision and machine learning